Initial commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaReachDense-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachDense-v2
+      type: PandaReachDense-v2
+    metrics:
+    - type: mean_reward
+      value: -10.38 +/- 1.81
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **PandaReachDense-v2**
+This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+
+## Usage (with Stable-baselines3)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

a2c-PandaReachDense-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1835ec8e4ec5ecea76b7fdebcf3dd53ba5d3eb0d95a894cfb689e30666d3d64c
+size 108024

a2c-PandaReachDense-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

a2c-PandaReachDense-v2/data

@@ -0,0 +1,94 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=",
+        "__module__": "stable_baselines3.common.policies",
+        "__doc__": "\n    MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space (Tuple)\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Uses the CombinedExtractor\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ",
+        "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7fd3dd292af0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fd3dd286b40>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=",
+        "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
+        "optimizer_kwargs": {
+            "alpha": 0.99,
+            "eps": 1e-05,
+            "weight_decay": 0
+        }
+    },
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.dict.Dict'>",
+        ":serialized:": "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",
+        "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])",
+        "_shape": null,
+        "dtype": null,
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            3
+        ],
+        "low": "[-1. -1. -1.]",
+        "high": "[1. 1. 1.]",
+        "bounded_below": "[ True  True  True]",
+        "bounded_above": "[ True  True  True]",
+        "_np_random": null
+    },
+    "n_envs": 4,
+    "num_timesteps": 21820,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1677716119027388582,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 0.53091794  0.5200983  -0.24571653]\n [ 0.31265146 -0.97259796  0.49570414]\n [ 0.5998537   0.65763706 -0.9012449 ]\n [ 0.22956663 -0.63248944 -0.32797807]]",
+        "desired_goal": "[[ 0.24076733  0.57609636 -1.3793833 ]\n [ 0.09790036 -0.79378265  1.0794665 ]\n [ 1.131154    0.9998728  -0.6736258 ]\n [-0.915023   -0.1466498  -0.74403   ]]",
+        "observation": "[[ 0.53091794  0.5200983  -0.24571653 -0.16930895 -0.2287045  -2.3717165 ]\n [ 0.31265146 -0.97259796  0.49570414  0.239189   -1.1132867   0.95837533]\n [ 0.5998537   0.65763706 -0.9012449   0.76609117 -0.11115841 -0.15265824]\n [ 0.22956663 -0.63248944 -0.32797807 -0.7038066  -0.12210898 -0.76249945]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]",
+        "desired_goal": "[[ 0.05078135 -0.00369118  0.10326264]\n [-0.00911496 -0.06349289  0.02448751]\n [ 0.149197    0.06140886  0.16583091]\n [ 0.10016979 -0.02454643  0.01122866]]",
+        "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"
+    },
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": 0.97818,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 1091,
+    "n_steps": 5,
+    "gamma": 0.99,
+    "gae_lambda": 1.0,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "normalize_advantage": false
+}

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f25f68104ffcfda2379758f56ae9ce9efce87590b8cb2af6f1c80b8c14fe65c9
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5b55391244a48812c9940b79a09b2926a569abea8ecc2dc425e518ba7475a6f6
+size 46014

a2c-PandaReachDense-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

a2c-PandaReachDense-v2/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space (Tuple)\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Uses the CombinedExtractor\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ", "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7fd3dd292af0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fd3dd286b40>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=", "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "observation_space": {":type:": "<class 'gym.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "bounded_below": "[ True  True  True]", "bounded_above": "[ True  True  True]", "_np_random": null}, "n_envs": 4, "num_timesteps": 21820, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1677716119027388582, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.53091794  0.5200983  -0.24571653]\n [ 0.31265146 -0.97259796  0.49570414]\n [ 0.5998537   0.65763706 -0.9012449 ]\n [ 0.22956663 -0.63248944 -0.32797807]]", "desired_goal": "[[ 0.24076733  0.57609636 -1.3793833 ]\n [ 0.09790036 -0.79378265  1.0794665 ]\n [ 1.131154    0.9998728  -0.6736258 ]\n [-0.915023   -0.1466498  -0.74403   ]]", "observation": "[[ 0.53091794  0.5200983  -0.24571653 -0.16930895 -0.2287045  -2.3717165 ]\n [ 0.31265146 -0.97259796  0.49570414  0.239189   -1.1132867   0.95837533]\n [ 0.5998537   0.65763706 -0.9012449   0.76609117 -0.11115841 -0.15265824]\n [ 0.22956663 -0.63248944 -0.32797807 -0.7038066  -0.12210898 -0.76249945]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]", "desired_goal": "[[ 0.05078135 -0.00369118  0.10326264]\n [-0.00911496 -0.06349289  0.02448751]\n [ 0.149197    0.06140886  0.16583091]\n [ 0.10016979 -0.02454643  0.01122866]]", "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"}, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.97818, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 1091, "n_steps": 5, "gamma": 0.99, "gae_lambda": 1.0, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": false, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -10.383922017924487, "std_reward": 1.8103775725480205, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-02T00:18:27.503703"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:36022c56a6e88e72cff06cf98c5af4581b324af0f3f0b09fa1fc7b3f0333b07b
+size 3056